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. 2023 Jun 21;23(13):5790.
doi: 10.3390/s23135790.

Detection of Defective Lettuce Seedlings Grown in an Indoor Environment under Different Lighting Conditions Using Deep Learning Algorithms

Munirah Hayati Hamidon  1 , Tofael Ahamed  2
Affiliations

Detection of Defective Lettuce Seedlings Grown in an Indoor Environment under Different Lighting Conditions Using Deep Learning Algorithms

Munirah Hayati Hamidon et al. Sensors (Basel). .

Abstract

Sorting seedlings is laborious and requires attention to identify damage. Separating healthy seedlings from damaged or defective seedlings is a critical task in indoor farming systems. However, sorting seedlings manually can be challenging and time-consuming, particularly under complex lighting conditions. Different indoor lighting conditions can affect the visual appearance of the seedlings, making it difficult for human operators to accurately identify and sort the seedlings consistently. Therefore, the objective of this study was to develop a defective-lettuce-seedling-detection system under different indoor cultivation lighting systems using deep learning algorithms to automate the seedling sorting process. The seedling images were captured under different indoor lighting conditions, including white, blue, and red. The detection approach utilized and compared several deep learning algorithms, specifically CenterNet, YOLOv5, YOLOv7, and faster R-CNN to detect defective seedlings in indoor farming environments. The results demonstrated that the mean average precision (mAP) of YOLOv7 (97.2%) was the highest and could accurately detect defective lettuce seedlings compared to CenterNet (82.8%), YOLOv5 (96.5%), and faster R-CNN (88.6%). In terms of detection under different light variables, YOLOv7 also showed the highest detection rate under white and red/blue/white lighting. Overall, the detection of defective lettuce seedlings by YOLOv7 shows great potential for introducing automated seedling-sorting systems and classification under actual indoor farming conditions. Defective-seedling-detection can improve the efficiency of seedling-management operations in indoor farming.

Keywords: CenterNet; YOLO; deep learning; faster RCNN; indoor farming; lettuce; seedling detection.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Seedling image acquisition process and development of the detection system.
Figure 2
Figure 2
Lettuce seedling growth system under different indoor lighting settings.
Figure 3
Figure 3
Example of captured images of lettuce seedlings under (a) white fluorescent; (b) red/blue LEDs; and (c) red/blue/white LEDs.
Figure 4
Figure 4
Different image augmentation methods: (a) original image; (b) 90° clockwise rotation; (c) 180° clockwise rotation; (d) horizontal flip; and (e) vertical flip.
Figure 5
Figure 5
Overall framework for the detection of defective lettuce seedlings using the CenterNet, YOLO, and faster R-CNN models.
Figure 6
Figure 6
The CenterNet prediction process in detecting defective seedlings based on triplet key points.
Figure 7
Figure 7
The YOLO prediction process in detecting defective seedlings based on bounding boxes.
Figure 8
Figure 8
The faster R-CNN prediction process in detecting defective seedlings based on the RPN and fast R-CNN detector.
Figure 9
Figure 9
Confusion matrices for detecting defective seedlings.
Figure 10
Figure 10
Training performance of datasets using the YOLOv5 algorithm.
Figure 11
Figure 11
Training performance of datasets using the YOLOv7 algorithm.
Figure 12
Figure 12
Test examples of the detection of defective lettuce seedlings under white light conditions from (a,b) original image; (c,d) CenterNet; (e,f) YOLOv5; (g,h) YOLOv7; (i,j) faster R-CNN; and (k) summary of the detection results. (The square mark in the figure refers to detected seedlings, and the circle mark indicates false and misdetected seedlings.)
Figure 12
Figure 12
Test examples of the detection of defective lettuce seedlings under white light conditions from (a,b) original image; (c,d) CenterNet; (e,f) YOLOv5; (g,h) YOLOv7; (i,j) faster R-CNN; and (k) summary of the detection results. (The square mark in the figure refers to detected seedlings, and the circle mark indicates false and misdetected seedlings.)
Figure 12
Figure 12
Test examples of the detection of defective lettuce seedlings under white light conditions from (a,b) original image; (c,d) CenterNet; (e,f) YOLOv5; (g,h) YOLOv7; (i,j) faster R-CNN; and (k) summary of the detection results. (The square mark in the figure refers to detected seedlings, and the circle mark indicates false and misdetected seedlings.)
Figure 13
Figure 13
Test examples of the detection of defective lettuce seedlings under red and blue light conditions from (a,b) original image; (c,d) CenterNet; (e,f) YOLOv5; (g,h) YOLOv7; (i,j) faster R-CNN; and (k) summary of the detection results. (The square mark in the figure refers to detected seedlings, and the circle mark indicates false and misdetected seedlings.)
Figure 13
Figure 13
Test examples of the detection of defective lettuce seedlings under red and blue light conditions from (a,b) original image; (c,d) CenterNet; (e,f) YOLOv5; (g,h) YOLOv7; (i,j) faster R-CNN; and (k) summary of the detection results. (The square mark in the figure refers to detected seedlings, and the circle mark indicates false and misdetected seedlings.)
Figure 13
Figure 13
Test examples of the detection of defective lettuce seedlings under red and blue light conditions from (a,b) original image; (c,d) CenterNet; (e,f) YOLOv5; (g,h) YOLOv7; (i,j) faster R-CNN; and (k) summary of the detection results. (The square mark in the figure refers to detected seedlings, and the circle mark indicates false and misdetected seedlings.)
Figure 14
Figure 14
Test examples of the detection of defective lettuce seedlings under red, blue, and white light conditions from (a,b) original image; (c,d) CenterNet; (e,f) YOLOv5; (g,h) YOLOv7; (i,j) faster R-CNN; and (k) summary of the detection results. (The square mark in the figure refers to detected seedlings, and the circle mark indicates false and misdetected seedlings.)
Figure 14
Figure 14
Test examples of the detection of defective lettuce seedlings under red, blue, and white light conditions from (a,b) original image; (c,d) CenterNet; (e,f) YOLOv5; (g,h) YOLOv7; (i,j) faster R-CNN; and (k) summary of the detection results. (The square mark in the figure refers to detected seedlings, and the circle mark indicates false and misdetected seedlings.)
Figure 14
Figure 14
Test examples of the detection of defective lettuce seedlings under red, blue, and white light conditions from (a,b) original image; (c,d) CenterNet; (e,f) YOLOv5; (g,h) YOLOv7; (i,j) faster R-CNN; and (k) summary of the detection results. (The square mark in the figure refers to detected seedlings, and the circle mark indicates false and misdetected seedlings.)
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